Intelligent Design

Business prediction model for actuarial systems to forecast payment defaults.

Technologies Leveraged

Python

Neural Networks

Scikit-learn, NumPy, Pandas, Keras

We have developed a prediction model for financial institutions invested in loan systems to forecast payment defaults.

Background

This is an inhouse initiative by Etasens

The Problem

Loan defaults is a major risk for financial institutions who have exposure to personal loans. With the use of predictive analytics, we aim to predict the performance of these loans. This will help the financial institutions hedge risk.

Solution

Deep learning algorithms have been used to develop a solution to predict if a customer will default on the payment. Sample loan datasets from ‘kaggle datasets’ were used as historical data input for training and validation of system. Feature extraction and pre-processing of input data was done using NumPy , Pandas and scikit-learn . Neural networks modelling is used to predict and evaluate the outcome. The solution was able to predict with 93.10 % accuracy on an average.

Result

System is expected to help banks and other financial institutions reduce the consumption of economic capital by decreasing the risk to the financial investor.